Re: [Idnet] IDN dedicated session call for case

Simone Ferlin <simone@ferlin.io> Wed, 09 August 2017 02:30 UTC

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From: Simone Ferlin <simone@ferlin.io>
Date: Wed, 9 Aug 2017 11:28:40 +0900
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To: Stenio Fernandes <sflf@cin.ufpe.br>
Cc: =?UTF-8?B?SsOpcsO0bWUgRnJhbsOnb2lz?= <jerome.francois@inria.fr>, yanshen <yanshen@huawei.com>, "idnet@ietf.org" <idnet@ietf.org>, Albert Cabellos <albert.cabellos@gmail.com>, "Diego R. Lopez" <diego.r.lopez@telefonica.com>
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Subject: Re: [Idnet] IDN dedicated session call for case
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Dear Jerome,

Very interesting use-case, +1 support. I have interest in such
activities for traffic classification, anomaly detection in particular
for encrypted traffic.


> On Wed, Aug 9, 2017 at 12:20 AM, Stenio Fernandes <sflf@cin.ufpe.br> wrote:
>> Hi Jerome, Diego, et al,
>>
>> Those are excellent use cases. I have some published work on applied
>> machine learning to computer networking problems, including flow-based
>> traffic classification. I think another use case would be applying
>> unsupervised learning techniques for anomaly detection. I can
>> elaborate further on this.
>>
>> Stenio
>>
>> On Tue, Aug 8, 2017 at 10:59 AM, Jérôme François
>> <jerome.francois@inria.fr> wrote:
>>> 100% agree with you. I was far from being exhaustive as traffic features may
>>> depend on types of traffic (kin of sub use cases)
>>>
>>> jerome
>>>
>>> Le 08/08/2017 à 16:56, Diego R. Lopez a écrit :
>>>
>>> Hi Jerome,
>>>
>>>
>>>
>>> Agreed. This is a use case we are very much interested in, and actually
>>> working in it now. Just let me say we are trying to evaluate which are the
>>> significant features of the flow to perform a proper classification,
>>> depending on the flow nature (TLS, DTLS, QUIC, IPsec…), and that would
>>> define the concrete data to be exchanged or stored.
>>>
>>>
>>>
>>> Be goode,
>>>
>>>
>>>
>>> --
>>>
>>> "Esta vez no fallaremos, Doctor Infierno"
>>>
>>>
>>>
>>> Dr Diego R. Lopez
>>>
>>> Telefonica I+D
>>>
>>> http://people.tid.es/diego.lopez/
>>>
>>>
>>>
>>> e-mail: diego.r.lopez@telefonica.com
>>>
>>> Tel:        +34 913 129 041
>>>
>>> Mobile: +34 682 051 091
>>>
>>> -----------------------------------
>>>
>>>
>>>
>>> On 8/8/2017, 16:49 , "IDNET on behalf of Jérôme François"
>>> <idnet-bounces@ietf.org on behalf of jerome.francois@inria.fr> wrote:
>>>
>>>
>>>
>>> Hi all,
>>>
>>> Here is another use case about traffic classification.
>>>
>>> Use case N+3: (encrypted) traffic classification
>>>
>>>     Description: collect flow-level traffic metrics such as protocol
>>> information but also meta metrics such as distribution of packet sizes,
>>> inter-arrival times... Then use such information to label the trafic with
>>> the underlying application assuming that the granularity of classification
>>> may vary (type of application, exact application name, version...)
>>>     Process: 1. collect packet information 2. flow reassembly (using
>>> directly flow format such as IPFIX might be possible but depends on the type
>>> of traffic, e.g. extracting the TLS application data is useful for encrypted
>>> traffic) 3. Collect application specific information (useful when targeting
>>> a single type of application) = out of network information 4. train the
>>> model 5. Online or offline testing 4. Apply application level policies.
>>>     Data Format:    Time : [Start, End, Unit, Number of Value, Sampling
>>> Period]
>>>                                 Position: [Device ID, Port ID]
>>>                                 Direction: IN / OUT
>>>                                 Flow level metric: packet size
>>> distributions, number of packets, inter-arrival time distribution,
>>>                                  (+ application specific knowledge : payload
>>> parsing)
>>>
>>>     Message :       Request: ask for the data
>>>                            Reply: Data
>>>                            Notice: For notification or others
>>>                            Policy: Control policy
>>>
>>>
>>> Best regards,
>>> jerome
>>>
>>>
>>> Le 08/08/2017 à 06:52, Albert Cabellos a écrit :
>>>
>>> Hi all
>>>
>>>
>>>
>>> Here´s another use-case:
>>>
>>>
>>>
>>> Use case N+2: QoE
>>>         Description: Collect low-level metrics (SNR, latency, jitter,
>>> losses, etc) and measure QoE. Then use ML to understand what is the relation
>>> between satisfactory QoE and the low-level metrics. As an example learn that
>>> when delay>N then QoE is degraded, but when M<delay<N then QoE is
>>> satisfactory for the customers (please note that QoE cannot be measured
>>> directly over your network). This is useful to understand how the network
>>> must be operated to provide satisfactory QoE.
>>>         Process: 1. Low-level data collection and QoE measurement ; 2.
>>> Training Model (input low-level metrics, output QoE); 3. Real-time data
>>> capture and input; 4. Predict QoE; 5. Operate network to meet target QoE
>>> requirement, go to 3.
>>>         Data Format:    Time : [Start, End, Unit, Number of Value, Sampling
>>> Period]
>>>                                 Position: [Device ID, Port ID]
>>>                                 Direction: IN / OUT
>>>                                 Low-level metric : SNR, Delay, Jitter,
>>> queue-size, etc
>>>
>>>
>>>         Message :       Request: ask for the data
>>>                                 Reply: Data
>>>                                 Notice: For notification or others
>>>                                 Policy: Control policy
>>>
>>>
>>>
>>> Kind regards
>>>
>>>
>>>
>>> Albert
>>>
>>>
>>>
>>> On Wed, Aug 2, 2017 at 7:12 PM, yanshen <yanshen@huawei.com> wrote:
>>>
>>> Dear all,
>>>
>>> Since we plan to organize a dedicated session in NMRG, IETF100, for applying
>>> AI into network management (NM), I’d try to list some Use Cases and propose
>>> a roadmap and ToC before Nov.
>>>
>>> These might be rough. You are welcome to refine them and propose your
>>> focused use cases or ideas.
>>>
>>> Use case 1: Traffic Prediction
>>>         Description: Collect the history traffic data and external data
>>> which may influence the traffic. Predict the traffic in short/long/specific
>>> term. Avoid the congestion or risk in previously.
>>>         Process: 1. Data collection (e.g. traffic sample of physical/logical
>>> port ); 2. Training Model; 3. Real-time data capture and input; 4.
>>> Predication output; 5. Fix error and go back to 3.
>>>         Data Format:    Time : [Start, End, Unit, Number of Value, Sampling
>>> Period]
>>>                                 Position: [Device ID, Port ID]
>>>                                 Direction: IN / OUT
>>>                                 Route : [R1, R2, ..., RN]  (might be useful
>>> for some scenarios)
>>>                                 Service : [Service ID, Priority, ...]  (Not
>>> clear how to use it but seems useful)
>>>                                 Traffic: [T0, T1, T2, ..., TN]
>>>         Message :       Request: ask for the data
>>>                                 Reply: Data
>>>                                 Notice: For notification or others
>>>                                 Policy: Control policy
>>>
>>> Use case 2: QoS Management
>>>         Description: Use multiple paths to distribute the traffic flows.
>>> Adjust the percentages. Avoid congestion and ensure QoS.
>>>         Process: 1. Data capture (e.g. traffic sample of physical/logical
>>> port ); 2. Training Model; 3. Real-time data capture and input; 4. Output
>>> percentages; 5. Fix error and go back to 3.
>>>         Data Format:    Time : [Timestamp, Value type (Delay/Packet
>>> Loss/...), Unit, Number of Value, Sampling Period]
>>>                                 Position: [Link ID, Device ID]
>>>                                 Value: [V0, V1, V2, ..., VN]
>>>         Message :       Request: ask for the data
>>>                                 Reply: Data
>>>                                 Notice: For notification or others
>>>                                 Policy: Control policy
>>>
>>> Use case N: Waiting for your Ideas
>>>
>>> Also I suggest a roadmap before Nov if possible.
>>>
>>> ### Roadmap ###
>>> Aug. : Collecting the use cases (related with NM). Rough thoughts and
>>> requirements
>>> Sep. : Refining the cases and abstract the common elements
>>> Oct. : Deeply analysis. Especially on Data Format, control flow, or other
>>> key points
>>> Nov.: F2F discussions on IETF100
>>> ### Roadmap End ###
>>>
>>> A rough ToC is listed in following. We may take it as a scope before Nov.
>>> Hope that the content could become the draft of draft.
>>>
>>> ###Table of Content###
>>> 1. Gap and Requirement Analysis
>>>         1.1 Network Management requirement
>>>         1.2 TBD
>>> 2. Use Cases
>>>         2.1 Traffic Prediction
>>>         2.2 QoS Management
>>>         3.3 TBD
>>> 3. Data Focus
>>>         3.1 Data attribute
>>>         3.2 Data format
>>>         3.3 TBD
>>> 4. Aims
>>>         4.1 Benchmarking Framework
>>>         4.2 TBD
>>> ###ToC End###
>>>
>>>
>>> Yansen
>>>
>>> _______________________________________________
>>> IDNET mailing list
>>> IDNET@ietf.org
>>> https://www.ietf.org/mailman/listinfo/idnet
>>>
>>>
>>>
>>>
>>>
>>>
>>> _______________________________________________
>>>
>>> IDNET mailing list
>>>
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>>>
>>> https://www.ietf.org/mailman/listinfo/idnet
>>>
>>>
>>>
>>>
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>>
>>
>>
>> --
>> Prof. Stenio Fernandes
>> CIn/UFPE
>> http://www.steniofernandes.com
>>
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